Purpose:Reconstructing CBCT only inside a relatively small circular field of view (FOV) is preferred for adaptive radiotherapy due to the reduced imaging dose, possible short computation time, and sufficient anatomy information for treatment adaptation. Current analytical or iterative reconstruction approaches encounter a truncation problem with degraded image quality inside the FOV. Anatomy outside the FOV is also missing, which is required for dose calculation. The purpose of this study is to develop a technique to obtain CBCT images with high quality inside the FOV and lower quality outside the FOV.

Methods:CBCT data acquisition is conducted first at all x-ray projection angles with a standard FOV to cover the whole body but at a very low mAs. A reconstruction is conducted via FDK algorithm, yielding a noisy CBCT image. X-ray projection is also acquired with a standard mAs for a small FOV of interest at sparse projection angles. We then forward project the noisy CBCT image outside the small FOV to those sparsely acquired projection angles and subtract the results from the high-mAs projection data. A high-quality CBCT image inside the FOV is reconstructed using the updated projection data at those sparse angles via our fast GPU-based tight-frame iterative CBCT reconstruction algorithm. Finally, the noisy image outside the FOV and the high-quality image inside the FOV are merged. Our algorithm is validated in digital NCAT phantom cases.

Results:It was found that the proposed method can remove the noise and truncation artifacts effectively and preserve anatomical features inside the FOV. It attains a relative root mean square (RRMS) error of 5.94%. Because of GPU implementation, the computational efficiency is high for clinical applications.